The subject matter disclosed herein generally relates to aircraft landing zone classification, and more particularly to context-aware landing zone classification for an aircraft.
Optionally-piloted vehicles (OPVs) and unmanned aerial vehicles (UAVs) can operate without a human pilot using autonomous controls. As OPVs and UAVs become more prevalent, they are being operated in less restricted and controlled areas. When OPVs and UAVs are operated autonomously in flight, they must identify a landing zone prior to landing. To account for unpredictable landing zone conditions, OPVs and UAVs typically use an image-based system to identify geometric factors that may impede a safe landing. Current art on autonomous landing zone detection has focused on three-dimensional (3D) terrain-based data acquisition modalities, such as LIght Detection and Ranging scanners (LIDAR), LAser Detection and Ranging scanners (LADAR), and RAdio Detection And Ranging (RADAR) for autonomous landing zone detection. While images can be valuable in identifying a safe landing zone, geometric factors may not provide enough information to determine whether a seemingly flat surface is a suitable landing site. For example, it may be difficult for image-based systems to discriminate between a dry field, a surface of a body of water, or a building top from only image information.